arXiv:2503.13939v5 Announce Type: replace 
Abstract: Vision-language models (VLMs) have achieved impressive progress in natural image reasoning, yet their potential in medical imaging remains underexplored. Medical vision-language tasks demand precise understanding and clinically coherent answers, which are difficult to achieve due to the complexity of medical data and the scarcity of high-quality expert annotations. These challenges limit the effectiveness of conventional supervised fine-tuning (SFT) and Chain-of-Thought (CoT) strategies that work well in general domains. To address these challenges, we propose Med-R1, a reinforcement learning (RL)-enhanced vision-language model designed to improve generalization and reliability in medical reasoning. Built on the DeepSeek strategy, Med-R1 adopts Group Relative Policy Optimization (GRPO) to encourage reward-guided learning beyond static annotations. We comprehensively evaluate Med-R1 across eight distinct medical imaging modalities. Med-R1 achieves a 29.94% improvement in average accuracy over its base model Qwen2-VL-2B, and even outperforms Qwen2-VL-72B-a model with 36x more parameters. To assess cross-task generalization, we further evaluate Med-R1 on five question types. Med-R1 outperforms Qwen2-VL-2B by 32.06% in question-type generalization, also surpassing Qwen2-VL-72B. We further explore the thinking process in Med-R1, a crucial component for the success of Deepseek-R1. Our results show that omitting intermediate rationales (No-Thinking-Med-R1) not only improves in-domain and cross-domain generalization with less training, but also challenges the assumption that more reasoning always helps. These findings suggest that in medical VQA, it is not reasoning itself, but its quality and domain alignment, that determine effectiveness. Together, these results highlight that RL improves medical reasoning and generalization, enabling efficient and reliable VLMs for real-world deployment.

يعد التطور الأخير لـ Med-R1، وهو نموذج تعلم معزز للمنطق الطبي في مهام الرؤية-اللغة، تقدمًا كبيرًا في مجال التصوير الطبي. على الرغم من أن نماذج الرؤية-اللغة أظهرت وعدًا كبيرًا في التفكير في الصور العامة، إلا أن تطبيقها في الطب كان محدودًا بسبب تعقيد البيانات الطبية ونقص التعليقات من الخبراء. يهدف Med-R1 إلى سد هذه الفجوة من خلال تحسين قدرة النموذج على تقديم إجابات متماسكة سريريًا، وهو أمر حاسم لتحسين دقة التشخيص ورعاية المرضى. يمكن أن تؤدي هذه الابتكارات إلى أدوات أكثر فعالية للمهنيين الصحيين، مما يعود بالنفع في النهاية على نتائج المرضى.

El reciente desarrollo de Med-R1, un modelo de aprendizaje por refuerzo para el razonamiento médico en tareas de visión-lenguaje, marca un avance significativo en el campo de la imagen médica. Aunque los modelos de visión-lenguaje han mostrado gran promesa en el razonamiento de imágenes generales, su aplicación en medicina ha sido limitada debido a la complejidad de los datos médicos y la falta de anotaciones de expertos. Med-R1 busca cerrar esta brecha al mejorar la capacidad del modelo para proporcionar respuestas clínicamente coherentes, lo cual es crucial para mejorar la precisión diagnóstica y la atención al paciente. Esta innovación podría llevar a herramientas más efectivas para los profesionales de la salud, beneficiando en última instancia los resultados de los pacientes.

Le développement récent de Med-R1, un modèle d'apprentissage par renforcement pour le raisonnement médical dans les tâches de vision-langage, marque une avancée significative dans le domaine de l'imagerie médicale. Bien que les modèles de vision-langage aient montré un grand potentiel dans le raisonnement d'images générales, leur application en médecine a été limitée en raison de la complexité des données médicales et du manque d'annotations d'experts. Med-R1 vise à combler cette lacune en améliorant la capacité du modèle à fournir des réponses cliniquement cohérentes, ce qui est crucial pour améliorer la précision diagnostique et les soins aux patients. Cette innovation pourrait conduire à des outils plus efficaces pour les professionnels de la santé, bénéficiant finalement aux résultats des patients.

The recent development of Med-R1, a reinforcement learning model for medical reasoning in vision-language tasks, marks a significant advancement in the field of medical imaging. While vision-language models have shown great promise in general image reasoning, their application in medicine has been limited due to the complexity of medical data and the lack of expert annotations. Med-R1 aims to bridge this gap by enhancing the model's ability to provide clinically coherent answers, which is crucial for improving diagnostic accuracy and patient care. This innovation could lead to more effective tools for healthcare professionals, ultimately benefiting patient outcomes.

Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

arXiv:2601.06204v2 Announce Type: replace 
Abstract: Intelligent anomaly detection in dynamic visual environments requires reconciling real-time performance with semantic interpretability. Conventional approaches address only fragments of this challenge. Reconstruction-based models capture low-level deviations without contextual reasoning, object detectors provide speed but limited semantics, and large vision-language systems deliver interpretability at prohibitive computational cost. This work introduces a cascading multi-agent framework that unifies these complementary paradigms into a coherent and interpretable architecture. Early modules perform reconstruction-gated filtering and object-level assessment, while higher-level reasoning agents are selectively invoked to interpret semantically ambiguous events. The system employs adaptive escalation thresholds and a publish-subscribe communication backbone, enabling asynchronous coordination and scalable deployment across heterogeneous hardware. Extensive evaluation on large-scale monitoring data demonstrates that the proposed cascade achieves a threefold reduction in latency compared to direct vision-language inference, while maintaining high perceptual fidelity (PSNR = 38.3 dB, SSIM = 0.965) and consistent semantic labeling. The framework advances beyond conventional detection pipelines by combining early-exit efficiency, adaptive multi-agent reasoning, and explainable anomaly attribution, establishing a reproducible and energy-efficient foundation for scalable intelligent visual monitoring.

تم تقديم إطار عمل جديد للكشف عن الشذوذ متعدد الوكلاء في أنظمة المراقبة، باستخدام نماذج الرؤية-اللغة وتصنيف قائم على التضمين لتحسين الأداء في الوقت الحقيقي وقابلية التفسير الدلالي. تتكامل هذه الطريقة مع منهجيات مختلفة، بما في ذلك التصفية المعتمدة على إعادة البناء والتقييم على مستوى الكائن، لمعالجة تعقيدات الكشف عن الشذوذ في البيئات البصرية الديناميكية.

Se ha introducido un nuevo marco para la detección de anomalías en cascada mediante múltiples agentes en sistemas de vigilancia, utilizando modelos de visión-lenguaje y clasificación basada en embeddings para mejorar el rendimiento en tiempo real y la interpretabilidad semántica. Este enfoque integra diversas metodologías, incluyendo filtrado basado en reconstrucción y evaluaciones a nivel de objeto, para abordar las complejidades de la detección de anomalías en entornos visuales dinámicos.

Un nouveau cadre pour la détection d'anomalies en cascade à plusieurs agents dans les systèmes de surveillance a été introduit, utilisant des modèles de vision-langage et une classification basée sur l'embedding pour améliorer la performance en temps réel et l'interprétabilité sémantique. Cette approche intègre diverses méthodologies, y compris le filtrage basé sur la reconstruction et les évaluations au niveau des objets, pour répondre aux complexités de la détection d'anomalies dans des environnements visuels dynamiques.

A new framework for cascading multi-agent anomaly detection in surveillance systems has been introduced, utilizing vision-language models and embedding-based classification to enhance real-time performance and semantic interpretability. This approach integrates various methodologies, including reconstruction-gated filtering and object-level assessments, to address the complexities of detecting anomalies in dynamic visual environments.

Cascading multi-agent anomaly detection in surveillance systems via vision-language models and embedding-based classification

arXiv:2508.13680v3 Announce Type: replace-cross 
Abstract: We introduce VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark designed to evaluate how vision-language models (VLMs) interpret and reason over visual and textual information beyond English. VMMU consists of 2.5k multimodal questions across 7 tasks, covering a diverse range of problem contexts, including STEM problem solving, data interpretation, rule-governed visual reasoning, and abstract visual reasoning. All questions require genuine multimodal integration, rather than reliance on text-only cues or OCR-based shortcuts. We evaluate a diverse set of state-of-the-art proprietary and open-source VLMs on VMMU. Despite strong Vietnamese OCR performance, proprietary models achieve only 66% mean accuracy. Further analysis shows that the primary source of failure is not OCR, but instead multimodal grounding and reasoning over text and visual evidence. Code and data are available at https://vmmu.github.io.

تم تقديم VMMU، وهو معيار فيتنامي لفهم وتقييم المهام المتعددة متعددة الوسائط، بهدف تقييم قدرات نماذج اللغة والرؤية (VLM) في تفسير واستنتاج المعلومات المرئية والنصية باللغة الفيتنامية. يتضمن هذا المعيار 2.5 ألف سؤال متعدد الوسائط عبر سبع مهام متنوعة، مع التركيز على التكامل الحقيقي متعدد الوسائط بدلاً من الاعتماد على الإشارات النصية فقط.

La introducción de VMMU, un referente vietnamita para la evaluación de la comprensión y el razonamiento multimodal, tiene como objetivo evaluar las capacidades de los modelos de lenguaje y visión (VLM) para interpretar y razonar sobre información visual y textual en vietnamita. Este referente incluye 2.5k preguntas multimodales en siete tareas diversas, enfatizando la integración multimodal genuina en lugar de depender únicamente de pistas textuales.

L'introduction de VMMU, une référence vietnamienne pour l'évaluation de la compréhension et du raisonnement multimodal, vise à évaluer les capacités des modèles de langage et de vision (VLM) à interpréter et raisonner sur des informations visuelles et textuelles en vietnamien. Cette référence comprend 2,5k questions multimodales réparties sur sept tâches diverses, mettant l'accent sur une véritable intégration multimodale plutôt que sur des indices uniquement textuels.

The introduction of VMMU, a Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark, aims to assess the capabilities of vision-language models (VLMs) in interpreting and reasoning over visual and textual information in Vietnamese. This benchmark includes 2.5k multimodal questions across seven diverse tasks, emphasizing genuine multimodal integration rather than text-only cues.

VMMU: A Vietnamese Multitask Multimodal Understanding and Reasoning Benchmark

One More Thing in AI – Your Shortcut to AI Mastery

Med-R1: Reinforcement Learning for Generalizable Medical Reasoning in Vision-Language Models

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